A method to construct propagation-consistent wireless environment digital twins from sparse CSI by creating a geometry-prior Bayesian channel map and calibrating a scene-level EM property field via differentiable ray tracing.
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11 Pith papers cite this work. Polarity classification is still indexing.
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PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
Embedding Gaussian primitives into a ray tracing structure enables unified radio propagation simulation and view synthesis from visual-only reconstructions.
The paper provides an open-source configuration-driven simulator for sub-THz radio-stripe architectures that includes models for polymer microwave fiber, couplers, and configurable RF impairments.
An LLM-powered agentic framework autonomously designs competitive and sometimes superior explainable algorithms for wireless PHY and MAC layer tasks.
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
An ellipsoid-guided selective refinement algorithm improves radio-map fidelity in urban wireless digital twins by prioritizing refinement of a small subset of buildings using only low-fidelity models.
PointNeRT is a neural surrogate for ray tracing that ingests point clouds and sequentially predicts multipath propagation and attenuation under physics constraints.
A unified deep learning model predicts FR3 signal strength from FR1 data and sparse measurements to cut simulation and measurement costs in 6G networks.
Linear regression on ray-tracing data predicts 7 GHz outdoor channel coefficients with MAE 7.5155e-5 and RMSE 9.2861e-5, beating SVR and decision-tree regression.
citing papers explorer
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Propagation-Consistent Wireless Environment Digital Twin Construction Under Sparse Measurements
A method to construct propagation-consistent wireless environment digital twins from sparse CSI by creating a geometry-prior Bayesian channel map and calibrating a scene-level EM property field via differentiable ray tracing.
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PilotWiMAE: Pilot-Native Representation Learning for Wireless Channels
PilotWiMAE pretrains an encoder on noisy pilots with factorized attention, 99% masking, patch-normalized reconstruction, scale loss, and AWGN curriculum to outperform supervised baselines in cross-frequency beam selection and channel tasks from 3.5 GHz pretraining to 28 GHz evaluation.
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Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis
Embedding Gaussian primitives into a ray tracing structure enables unified radio propagation simulation and view synthesis from visual-only reconstructions.
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An Open-Source Hardware-Aware Sub-THz Radio-Stripe Simulator
The paper provides an open-source configuration-driven simulator for sub-THz radio-stripe architectures that includes models for polymer microwave fiber, couplers, and configurable RF impairments.
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The AI Telco Engineer: Toward Autonomous Discovery of Wireless Communications Algorithms
An LLM-powered agentic framework autonomously designs competitive and sometimes superior explainable algorithms for wireless PHY and MAC layer tasks.
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Telecom World Models: Unifying Digital Twins, Foundation Models, and Predictive Planning for 6G
Telecom World Models introduce a three-layer architecture for learned, action-conditioned, uncertainty-aware modeling of 6G network dynamics, combining digital twins and foundation models, with a network slicing proof-of-concept showing improved KPI prediction over baselines.
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Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins
An ellipsoid-guided selective refinement algorithm improves radio-map fidelity in urban wireless digital twins by prioritizing refinement of a small subset of buildings using only low-fidelity models.
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PointNeRT: A Physics Aware Neural Ray Tracing Surrogate for Propagation Channel Modeling
PointNeRT is a neural surrogate for ray tracing that ingests point clouds and sequentially predicts multipath propagation and attenuation under physics constraints.
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CommUNext: Deep Learning-Based Cross-Band and Multi-Directional Signal Prediction
A unified deep learning model predicts FR3 signal strength from FR1 data and sparse measurements to cut simulation and measurement costs in 6G networks.
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Data driven approach for Outdoor Channel Prediction in 5G and Beyond
Linear regression on ray-tracing data predicts 7 GHz outdoor channel coefficients with MAE 7.5155e-5 and RMSE 9.2861e-5, beating SVR and decision-tree regression.
- FARM: Foundational Aerial Radio Map for Intelligent Low-Altitude Networking